A. Mesut Erzurumluoglu
I’m a Senior Scientist in the Human Genetics team of Boehringer Ingelheim (BI) Pharma, where I contribute to the drug target identification, (in)validation and repurposing efforts of the company using large-scale human genetics data. I am also the Product Owner (PO) of BI Digital Innovation Unit's biobank initiatives.
Before this, I worked as a Sn. Research Associate (Point 49) in Genetic Epidemiology at the MRC Epidemiology Unit, University of Cambridge (Jan 2019-Sept 2021), where we (leads: Prof. Claudia Langenberg & Prof. Nick Wareham) mainly studied the genetic aetiology of common metabolic disorders such as type-2 diabetes and obesity; and as a Research Associate in Genetic Epidemiology at the University of Leicester (Nov 2015-Jan 2019), where we (leads: Prof. Martin Tobin & Prof. Louise Wain) were mainly interested in the genetic aetiology of common respiratory diseases (e.g. chronic obstructive pulmonary disease) and related traits (e.g. lung function, smoking behaviour, blood pressure).
Previously (Feb 2012-Dec 2015), I did a PhD in Genetic Epidemiology at the University of Bristol (with 4-year full scholarship from the MRC; supervised by Dr Santi Rodriguez, Prof. Tom Gaunt, and Prof. Ian Day) where I mainly analysed whole-exome sequencing data obtained from consanguineous individual/families, and tried to identify (novel) causal variants of rare human diseases (e.g. Primary ciliary dyskinesia, Papillon-Lefevre syndrome). I also took part in many Population genetics, and statistical genetics-related projects.
Twitter: @mesuturkiye
Blog: bit.ly/mesutblog
NB: I can only share my 'open access' publications here. I am happy to share other papers directly though. Please see my Google Scholar page for the full list
Supervisors: Learn from everyone
Address: See CV
Before this, I worked as a Sn. Research Associate (Point 49) in Genetic Epidemiology at the MRC Epidemiology Unit, University of Cambridge (Jan 2019-Sept 2021), where we (leads: Prof. Claudia Langenberg & Prof. Nick Wareham) mainly studied the genetic aetiology of common metabolic disorders such as type-2 diabetes and obesity; and as a Research Associate in Genetic Epidemiology at the University of Leicester (Nov 2015-Jan 2019), where we (leads: Prof. Martin Tobin & Prof. Louise Wain) were mainly interested in the genetic aetiology of common respiratory diseases (e.g. chronic obstructive pulmonary disease) and related traits (e.g. lung function, smoking behaviour, blood pressure).
Previously (Feb 2012-Dec 2015), I did a PhD in Genetic Epidemiology at the University of Bristol (with 4-year full scholarship from the MRC; supervised by Dr Santi Rodriguez, Prof. Tom Gaunt, and Prof. Ian Day) where I mainly analysed whole-exome sequencing data obtained from consanguineous individual/families, and tried to identify (novel) causal variants of rare human diseases (e.g. Primary ciliary dyskinesia, Papillon-Lefevre syndrome). I also took part in many Population genetics, and statistical genetics-related projects.
Twitter: @mesuturkiye
Blog: bit.ly/mesutblog
NB: I can only share my 'open access' publications here. I am happy to share other papers directly though. Please see my Google Scholar page for the full list
Supervisors: Learn from everyone
Address: See CV
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Papers by A. Mesut Erzurumluoglu
Results: In this manuscript, we describe LD Hub-a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP herit-ability and the genetic correlation across the different phenotypes. We also present new results obtained by 2 uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. Availability and implementation: The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/
Mutations in the gene MTARC1 (mitochondrial amidoxime–reducing component 1) protect carriers from metabolic dysfunction–associated steatohepatitis (MASH) and cirrhosis. MTARC1 encodes the mARC1 enzyme, which is localized to the mitochondria and has no known MASH-relevant molecular function. Our studies aimed to expand on the published human genetic mARC1 data and to observe the molecular effects of mARC1 modulation in preclinical MASH models.
Methods and Results:
We identified a novel human structural variant deletion in MTARC1, which is associated with various biomarkers of liver health, including alanine aminotransferase levels. Phenome-wide Mendelian Randomization analyses additionally identified novel putatively causal associations between MTARC1 expression, and esophageal varices and cardiorespiratory traits. We observed that protective MTARC1 variants decreased protein accumulation in in vitro overexpression systems and used genetic tools to study mARC1 depletion in relevant human and mouse systems. Hepatocyte mARC1 knockdown in murine MASH models reduced body weight, liver steatosis, oxidative stress, cell death, and fibrogenesis markers. mARC1 siRNA treatment and overexpression modulated lipid accumulation and cell death consistently in primary human hepatocytes, hepatocyte cell lines, and primary human adipocytes. mARC1 depletion affected the accumulation of distinct lipid species and the expression of inflammatory and mitochondrial pathway genes/proteins in both in vitro and in vivo models.
Conclusions:
Depleting hepatocyte mARC1 improved metabolic dysfunction–associated steatotic liver disease–related outcomes. Given the functional role of mARC1 in human adipocyte lipid accumulation, systemic targeting of mARC1 should be considered when designing mARC1 therapies. Our data point to plasma lipid biomarkers predictive of mARC1 abundance, such as Ceramide 22:1. We propose future areas of study to describe the precise molecular function of mARC1, including lipid trafficking and subcellular location within or around the mitochondria and endoplasmic reticulum.
Methods Counties in the USA were categorised into five groups by level of social vulnerability, using the Social Vulnerability Index (a widely used measure of social disadvantage) developed by the US Centers for Disease Control and Prevention. The incidence and mortality from COVID-19, and the prevalence of major chronic conditions were calculated relative to the least vulnerable quintile using Poisson regression models.
Results Among 3141 counties, there were 5 010 496 cases and 161 058 deaths from COVID-19 by 10 August 2020. Relative to the least vulnerable quintile, counties in the most vulnerable quintile had twice the rates of COVID-19 cases and deaths (rate ratios 2.11 (95% CI 1.97 to 2.26) and 2.42 (95% CI 2.22 to 2.64), respectively). Similarly, the prevalence of major chronic conditions was 24%–41% higher in the most vulnerable counties. Geographical clustering of counties with high COVID-19 mortality, high chronic disease prevalence and high social vulnerability was found, especially in southern USA.
Conclusion Some counties are experiencing a confluence of epidemics from COVID-19 and chronic diseases in the context of social disadvantage. Such counties are likely to require enhanced public health and social support.
Males have greater cardiometabolic risk than females, though the reasons for this are poorly understood. The aim of this study was to examine the association between common Y chromosomal haplogroups and cardiometabolic risk during early life.
Methods
In a British birth cohort, we examined the association of Y chromosomal haplogroups with trajectories of cardiometabolic risk factors from birth to 18 years and with carotid-femoral pulse wave velocity, carotid intima media thickness and left ventricular mass index at age 18. Haplogroups were grouped according to their phylogenetic relatedness into categories of R, I, E, J, G and all other haplogroups combined (T, Q, H, L, C, N and O). Risk factors included BMI, fat and lean mass, systolic blood pressure (SBP), diastolic blood pressure, pulse rate, triglycerides, high density lipoprotein cholesterol (HDL-c), non-HDL-c and c-reactive protein. Analyses were performed using multilevel models and linear regression, as appropriate.
Results
Y chromosomal haplogroups were not associated with any cardiometabolic risk factors from birth to 18 years. For example, at age 18, the difference in SBP comparing each haplogroup with haplogroup R was −0.39 mmHg (95% Confidence Interval (CI): −0.75, 1.54) for haplogroup I, 2.56 mmHg (95% CI: −0.76, 5.89) for haplogroup E, −0.02 mmHg (95% CI: −2.87, 2.83) for haplogroup J, 1.28 mmHg (95% CI: −4.70, 2.13) for haplogroup G and −2.75 mmHg (95% CI: −6.38, 0.88) for all other haplogroups combined.
Conclusions
Common Y chromosomal haplogroups are not associated with cardiometabolic risk factors during childhood and adolescence or with subclinical cardiovascular measures at age 18.
Results: In this manuscript, we describe LD Hub-a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP herit-ability and the genetic correlation across the different phenotypes. We also present new results obtained by 2 uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. Availability and implementation: The web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/
Mutations in the gene MTARC1 (mitochondrial amidoxime–reducing component 1) protect carriers from metabolic dysfunction–associated steatohepatitis (MASH) and cirrhosis. MTARC1 encodes the mARC1 enzyme, which is localized to the mitochondria and has no known MASH-relevant molecular function. Our studies aimed to expand on the published human genetic mARC1 data and to observe the molecular effects of mARC1 modulation in preclinical MASH models.
Methods and Results:
We identified a novel human structural variant deletion in MTARC1, which is associated with various biomarkers of liver health, including alanine aminotransferase levels. Phenome-wide Mendelian Randomization analyses additionally identified novel putatively causal associations between MTARC1 expression, and esophageal varices and cardiorespiratory traits. We observed that protective MTARC1 variants decreased protein accumulation in in vitro overexpression systems and used genetic tools to study mARC1 depletion in relevant human and mouse systems. Hepatocyte mARC1 knockdown in murine MASH models reduced body weight, liver steatosis, oxidative stress, cell death, and fibrogenesis markers. mARC1 siRNA treatment and overexpression modulated lipid accumulation and cell death consistently in primary human hepatocytes, hepatocyte cell lines, and primary human adipocytes. mARC1 depletion affected the accumulation of distinct lipid species and the expression of inflammatory and mitochondrial pathway genes/proteins in both in vitro and in vivo models.
Conclusions:
Depleting hepatocyte mARC1 improved metabolic dysfunction–associated steatotic liver disease–related outcomes. Given the functional role of mARC1 in human adipocyte lipid accumulation, systemic targeting of mARC1 should be considered when designing mARC1 therapies. Our data point to plasma lipid biomarkers predictive of mARC1 abundance, such as Ceramide 22:1. We propose future areas of study to describe the precise molecular function of mARC1, including lipid trafficking and subcellular location within or around the mitochondria and endoplasmic reticulum.
Methods Counties in the USA were categorised into five groups by level of social vulnerability, using the Social Vulnerability Index (a widely used measure of social disadvantage) developed by the US Centers for Disease Control and Prevention. The incidence and mortality from COVID-19, and the prevalence of major chronic conditions were calculated relative to the least vulnerable quintile using Poisson regression models.
Results Among 3141 counties, there were 5 010 496 cases and 161 058 deaths from COVID-19 by 10 August 2020. Relative to the least vulnerable quintile, counties in the most vulnerable quintile had twice the rates of COVID-19 cases and deaths (rate ratios 2.11 (95% CI 1.97 to 2.26) and 2.42 (95% CI 2.22 to 2.64), respectively). Similarly, the prevalence of major chronic conditions was 24%–41% higher in the most vulnerable counties. Geographical clustering of counties with high COVID-19 mortality, high chronic disease prevalence and high social vulnerability was found, especially in southern USA.
Conclusion Some counties are experiencing a confluence of epidemics from COVID-19 and chronic diseases in the context of social disadvantage. Such counties are likely to require enhanced public health and social support.
Males have greater cardiometabolic risk than females, though the reasons for this are poorly understood. The aim of this study was to examine the association between common Y chromosomal haplogroups and cardiometabolic risk during early life.
Methods
In a British birth cohort, we examined the association of Y chromosomal haplogroups with trajectories of cardiometabolic risk factors from birth to 18 years and with carotid-femoral pulse wave velocity, carotid intima media thickness and left ventricular mass index at age 18. Haplogroups were grouped according to their phylogenetic relatedness into categories of R, I, E, J, G and all other haplogroups combined (T, Q, H, L, C, N and O). Risk factors included BMI, fat and lean mass, systolic blood pressure (SBP), diastolic blood pressure, pulse rate, triglycerides, high density lipoprotein cholesterol (HDL-c), non-HDL-c and c-reactive protein. Analyses were performed using multilevel models and linear regression, as appropriate.
Results
Y chromosomal haplogroups were not associated with any cardiometabolic risk factors from birth to 18 years. For example, at age 18, the difference in SBP comparing each haplogroup with haplogroup R was −0.39 mmHg (95% Confidence Interval (CI): −0.75, 1.54) for haplogroup I, 2.56 mmHg (95% CI: −0.76, 5.89) for haplogroup E, −0.02 mmHg (95% CI: −2.87, 2.83) for haplogroup J, 1.28 mmHg (95% CI: −4.70, 2.13) for haplogroup G and −2.75 mmHg (95% CI: −6.38, 0.88) for all other haplogroups combined.
Conclusions
Common Y chromosomal haplogroups are not associated with cardiometabolic risk factors during childhood and adolescence or with subclinical cardiovascular measures at age 18.
With the ever decreasing prices of DNA sequencing, whole-genome sequencing is becoming a reality for many laboratories. However, for now, whole-exome sequencing (WES) is the most feasible sequencing technique mostly due to cost factors. Combining the concepts of consanguinity and WES, the aim of this thesis was to identify ‘causal’ variants by analysing whole-exome data obtained from consanguineous families/individuals affected from autosomal recessive disorders such as Primary Ciliary Dyskinesia (PCD) and Autosomal Recessive Intellectual Disability (ARID). Using autozygosity mapping, a novel region located on chromosome 19 (p13.3) was identified to be associated with ARID (later sequenced by another research group and ADAT3 was identified as the causal gene). Using WES, rare homozygous nonsense mutations p.E309* in CCDC151 and p.R136* in DNAAF3 were found to be causal of PCD. Other variants such as p.M263T in MNS1, p.R263* in DNALI1, p.G734fs in HEATR2 and p.E328* in LRRC48 have also been identified which may be causal of PCD but studies in this thesis remained inconclusive due to various reasons. Additionally, a rare missense mutation p.G300D in CTSC was found to be Papillon-Lèfevre syndrome (PLS) causal. This latter finding illustrated the additional information that can be gained from WES data – which is discussed in the thesis.
Finding novel causal variants and gene functions can improve genetic counselling and lead to the identification of targets for preventive and/or curative medicine. In this respect, analysing consanguineous populations as a whole rather than ‘cherry picking’ families with disorders will have additional benefits and facilitate our understanding of the human genome – and this subject is also discussed in this thesis.
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I would like to share my own views on the question although it won’t be comprehensive as I don’t have much experience about how an idea can be taken to further to impact policy and public health practice.
Featured in my blog: https://mesuturkey.wordpress.com/2020/02/19/back-to-basics-prevention-through-education-of-parents/
“How did you get accepted to Cambridge?”
I saw a tweet a while ago which said something along the lines of: “If you’ve been asked the same question three times, you need to write a blog post about it”. I get asked about how I got my current postdoc job at the University of Cambridge all the time. Therefore, I decided to write this document to provide a bit of a backstory as I did many things over the years which – with a bit of luck – contributed to this achievement.
It is a long document but hopefully it will be worth reading in full for all foreign PhD students, new Postdocs and undergraduates who want an introduction to the world of academia in the UK. I wish I could write it in other languages (for a Turkish version click here) to make it as easy as I can for you, but I strived to use as less jargon as possible. Although there is some UK-specific information in there, the document is mostly filled with general guidance that will be applicable to not just foreign students or those who want to study in the UK, but all PhD students and new Postdocs.
I can only hope that there are no errors and every section is complete and fully understandable but please do contact me for clarifications, suggestions and/or criticism. I thank you in advance!
To make a connection between academia in the UK and Darwin’s quote above, I would say being very clever/intelligent is definitely an advantage in academia but it is not the be-all and end-all. Learning to adapt with the changing landscape (e.g. sought-after skills, priorities of funders and PIs), keeping a good relationship with your colleagues and supervisors, and being able to sell your yourself is as, if not more important. Those who pay attention to this side of academia usually make things easier for themselves.
I hope the below document helps you reach the places you want to reach. Good luck in your career!
“Cambridge Üniversitesi’ne nasıl kabul aldın?”
Twitter’da gördüm sanırım: “Aynı soru sana üç defa sorulduysa bir blog yazısı yazma vakti gelmiştir”e benzer bir cümleydi. Ben de “Cambridge Üniversitesi’ne nasıl kabul aldın?” ve benzeri sorularla pek çok defa karşılaştıktan sonra birşeyler karalamaya karar verdim. Leicester Üniversitesi’nde çalışırken bunun onda biri dahi sorulmamıştı 😉
Doktora öğrencilerine, doktorayı yeni bitirenlere ve akademik kariyer düşünen gençlere yönelik uzun bir doküman hazırladım. Az da olsa ingilizce terimler kullandım ama merak eden herkes okuyabilsin diye elimden geldikçe azaltmaya çalıştım (Not: iyi derecede ingilizce bilmeyenlerin iyi üniversitelere girmesi, hasbel-kader girdiyse de oralarda tutunması zor).
Okuyacağınız herşey benim şahsi düşüncelerim ve hiçbirine katılmak zorunda değilsiniz. Eminim yazdıklarımda hatalar ve eksikler olacaktır; bunları da bana bildirirseniz dökümanı hep beraber geliştirmiş oluruz. Katkıda bulunanlara da bir şekilde değineceğim. Şimdiden teşekkürler!
Darwin’in yukarıda paylaştığım sözüyle bir bağlantı kuracak olursam, evet, bir akademisyen için çok akıllı/zeki olmak bir avantajdır. Ama oyunun kurallarını (örneğin ‘arkadaşlarım/hocalarımla aramı nasıl iyi tutarım?‘, ‘iyi makale nasıl yazılır?‘, ‘nasıl fon getiririm?‘i) öğrenmek ve onlara göre adapte olmak da en az o kadar önemli – özellikle akademide oldugu gibi ‘oyun’un kuralları devamlı degişiyorsa… İşin bu kısımlarına da vakit harcayın.
Aşağıdaki dökümanda “Doktora sürecinde nelere dikkat etmeliyim?”, İngiltere’de akademik kariyer opsiyonları, “CV ve ‘Personal statement’ nasıl hazırlanır?“, ‘mülakat anı, öncesi ve sonrası neler yapmalıyım?‘, tez yazarken dikkat edilecekler, makale yazarken dikkat edilecekler ve prosedür, “Hocanızla ilişkiniz nasıl olmalı?” gibi konularda bilgiler ve tavsiyelerim bulunuyor. Umarım yardımcı olur. İlgileneceğini düşündüğünüz arkadaşlarınıza da yollarsanız sevinirim.
Blog: mesuturkey.wordpress.com
Twitter: @mesuturkiye
Feel free to download, print* and/or disseminate. I also plan to write it in Turkish if and when I have the time.
Important Note: Please do not edit my son’s images in any way or use it in another medium